Distributed learning for supervised multiview feature selection

摘要

Multiview feature selection technique is specifically designed to reduce the dimensionality of multiview data and has received much attention. Most proposed multiview supervised feature selection methods suffer from the problem of efficiently handling the large-scale and high-dimensional data. To address this, this paper designs an efficient supervised multiview feature selection method for multiclass problems by combining the distributed optimization method in the Alternating Direction Method of Multipliers (ADMM). Specifically, the distributed strategy is reflected in two aspects. On the one hand, a sample-partition based distributed strategy is adopted, which calculates the loss term of each category individually. On the other hand, a view-partition based distributed strategy is used to explore the consistent and characteristic information of views. We adopt the individual regularization on each view and the common loss term which is obtained by fusing different views to jointly share the label matrix. Benefited from the distributed framework, the model can realize a distributed solution for the transformation matrix and reduce the complexity for multiview feature selection. Extensive experiments have demonstrated that the proposed method achieves a great improvement on training time, and the comparable or better performance compared to several state-of-the-art supervised feature selection algorithms.